COM S 474

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Introduction to Machine Learning


Official Catalog Description

Basic principles, techniques, and applications of Machine Learning. Design, analysis, implementation, and applications of learning algorithms. Topics include: statistical learning, pattern classification, function approximation, Bayesian learning, linear models, artificial neural networks, support vector machines, decision trees, instance based learning, probabilistic graphical models, unsupervised learning, selected applications in automated knowledge acquisition, pattern recognition, and data mining.


Elaborated Description

General Course Structure

Course Materials

Other Information

  • Part of group 2 of the 400-level courses
  • 3 Credits
  • Offered odd-numbered years in Spring
  • Prereq: COM S 311, COM S 230 or CPR E 310, STAT 330, MATH 165, ENGL 250, SP CM 212, COM S 342 or comparable programming experience